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The main idea of Machine Learning is that your training data is similar to test data. If that hypothesis does not hold, then big chances of failure are guaranteed. Given that, you still can handle unseen categories even thought the solution is not good and more with One Hot Encoding In the category encoders library you have the following hyperparameter with ...


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You need to encode categorical variables as dummies. This means to create new features for each type of category and then assigned either a 1 (where a record has that category) or 0 (to each record that doesn't have that category). With some examples, this should look something like this good clean hotel enjoyed stay here I am ...


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In general, it "okay" to apply to binary encode high cardinality datasets. In the sense of it will create numerical features that can be learned by a machine learning model. However there are often better options, such a label encoding, frequency encoding, target encoding, or embeddings. It is an empirical question which encoding scheme is best for ...


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Technically your problem is not about a variable number of features, since you can have a finite list of all the possible features. The standard case is just to use all these features, even if only a few of them are "active" for a particular instance (your first option). If the number of features is too high, then you need dimensionality reduction. ...


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